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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.17161 |
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| _version_ | 1866929729095860224 |
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| author | Koenig, Michael Rauch, Jakob Woerter, Martin |
| author_facet | Koenig, Michael Rauch, Jakob Woerter, Martin |
| contents | Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, WAI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that WAI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, WAI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time Monitoring of Economic Shocks using Company Websites Koenig, Michael Rauch, Jakob Woerter, Martin General Economics Economics Artificial Intelligence Computation and Language Data Analysis, Statistics and Probability Understanding the effects of economic shocks on firms is critical for analyzing economic growth and resilience. We introduce a Web-Based Affectedness Indicator (WAI), a general-purpose tool for real-time monitoring of economic disruptions across diverse contexts. By leveraging Large Language Model (LLM) assisted classification and information extraction on texts from over five million company websites, WAI quantifies the degree and nature of firms' responses to external shocks. Using the COVID-19 pandemic as a specific application, we show that WAI is highly correlated with pandemic containment measures and reliably predicts firm performance. Unlike traditional data sources, WAI provides timely firm-level information across industries and geographies worldwide that would otherwise be unavailable due to institutional and data availability constraints. This methodology offers significant potential for monitoring and mitigating the impact of technological, political, financial, health or environmental crises, and represents a transformative tool for adaptive policy-making and economic resilience. |
| title | Real-time Monitoring of Economic Shocks using Company Websites |
| topic | General Economics Economics Artificial Intelligence Computation and Language Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2502.17161 |